Limits...
Level set method for positron emission tomography.

Chan TF, Li H, Lysaker M, Tai XC - Int J Biomed Imaging (2007)

Bottom Line: Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate.An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way.We utilize a multiple level set formulation to represent the geometry of the objects in the scene.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555, USA.

ABSTRACT
In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications.

No MeSH data available.


64 × 64 segmented MRI slice of the brain.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2266822&req=5

fig15: 64 × 64 segmented MRI slice of the brain.

Mentions: Assume that MRI or CT observations are used togenerate information of the PET phantom, partly or in the entire domain Ω —see[49, 50]. Below we will demonstratethat such information will improve the image reconstruction capacitynoticeably. First, we assume both ϕ1 and ϕ2 to be known,which means that all the boundaries are known a priori, and we just need torecover the piecewise constant intensity values of the image. The result isshown in Figure 15. Compared with the results in Figure 14, we see that a priorinformation of the geometrical objects improves the reconstructiondramatically. We need only about 20 iterations to reconstruct a perfect image.In this case, after 200 iterations, the intensity values were recovered prettywell as {0, 1.01, 3.98}.


Level set method for positron emission tomography.

Chan TF, Li H, Lysaker M, Tai XC - Int J Biomed Imaging (2007)

64 × 64 segmented MRI slice of the brain.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2266822&req=5

fig15: 64 × 64 segmented MRI slice of the brain.
Mentions: Assume that MRI or CT observations are used togenerate information of the PET phantom, partly or in the entire domain Ω —see[49, 50]. Below we will demonstratethat such information will improve the image reconstruction capacitynoticeably. First, we assume both ϕ1 and ϕ2 to be known,which means that all the boundaries are known a priori, and we just need torecover the piecewise constant intensity values of the image. The result isshown in Figure 15. Compared with the results in Figure 14, we see that a priorinformation of the geometrical objects improves the reconstructiondramatically. We need only about 20 iterations to reconstruct a perfect image.In this case, after 200 iterations, the intensity values were recovered prettywell as {0, 1.01, 3.98}.

Bottom Line: Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate.An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way.We utilize a multiple level set formulation to represent the geometry of the objects in the scene.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555, USA.

ABSTRACT
In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications.

No MeSH data available.